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Sustainability 2017, 9(7), 1161; doi:10.3390/su9071161

Carbon Reduction Potential of Resource-Dependent Regions Based on Simulated Annealing Programming Algorithm

School of Economics and Management, Taiyuan University of Technology, Taiyuan 030024, China
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Received: 18 May 2017 / Revised: 28 June 2017 / Accepted: 30 June 2017 / Published: 3 July 2017
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Abstract

In recent years, developing countries, especially resource-dependent regions, have been facing the paradox of ensuring both emissions reduction and economic development. Thus, there is a strong political desire to forecast carbon emissions reduction potential and the best way to achieve it. This study constructs a methodology to assess carbon reduction potential in a resource-dependent region. The Simulated Annealing Programming algorithm and the Genetic algorithm were introduced to create a prediction model and an optimized regional carbon intensity model, respectively. Shanxi Province in China, a typical resource-dependent area, is selected for the empirical study. Regional statistical data are collected from 1990 to 2015. The results show that the carbon intensity of Shanxi Province could drop 18.78% by 2020. This potential exceeds the 18% expectation of the Chinese Government in its ‘13th Five-Year Work Plan’ for Controlling Greenhouse Gas Emissions. Moreover, the carbon intensity of the province could be further reduced by 0.97 t per 10,000 yuan GDP. The study suggests that the carbon emissions of a resource-dependent region can be reduced in the following ways; promoting economic restructuring, upgrading coal supply-side reform, perfecting the self-regulation of coal prices, accelerating the technical innovation of the coal industry, and establishing a flexible mechanism for reducing emissions. View Full-Text
Keywords: resource-dependent regions; carbon reduction potential; carbon intensity; Simulated Annealing Programming; Shanxi Province resource-dependent regions; carbon reduction potential; carbon intensity; Simulated Annealing Programming; Shanxi Province
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Li, W.; Li, G.; Zhang, R.; Sun, W.; Wu, W.; Jin, B.; Cui, P. Carbon Reduction Potential of Resource-Dependent Regions Based on Simulated Annealing Programming Algorithm. Sustainability 2017, 9, 1161.

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